module_q.py

from pathlib import Path
from typing import List, Tuple
from langchain import PromptTemplate, LLMChain
from langchain.document_loaders import TextLoader
from langchain.embeddings import LlamaCppEmbeddings
from langchain.llms import GPT4All
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores.faiss import FAISS
from pydantic import BaseModel, Field
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import LlamaCpp
import os.path


import langchain
from langchain.cache import InMemoryCache
langchain.llm_cache = InMemoryCache()

# Constants
local_path = "./models/gpt4all-converted.bin" #GPT4 module 1
#local_path = "./models/ggml-gpt4all-l13b-snoozy.bin"


model_path = "./models/ggml-model-q4_0.bin" #1st Embeddings
#model_path = './models/ggjt-model.bin'  #2st Embedding
text_path = "./docs/acapglobal.txt"
index_path = "./acapglobal_index"


# Functions
def initialize_embeddings() -> LlamaCppEmbeddings:
    return LlamaCppEmbeddings(model_path=model_path)
    
def load_documents() -> List:
    loader = TextLoader(text_path,encoding="utf-8")
    return loader.load()

def split_chunks(sources: List) -> List:
    chunks = []
    splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=32)
    for chunk in splitter.split_documents(sources):
        chunks.append(chunk)
    return chunks

def generate_index(chunks: List, embeddings: LlamaCppEmbeddings) -> FAISS:
    texts = [doc.page_content for doc in chunks]
    metadatas = [doc.metadata for doc in chunks]
    return FAISS.from_texts(texts, embeddings, metadatas=metadatas)


# Main execution
llm = GPT4All(model=local_path, n_ctx=512, verbose=True,cache=True,embedding=True)
print('llm GPT4All set.')
embeddings = initialize_embeddings()


# when refresh document
#

# print('initialize_embeddings.')
# sources = load_documents()
# print('load_documents.')
# chunks = split_chunks(sources)
# print('split_chunks')
# vectorstore = generate_index(chunks, embeddings)
# print('generate_index')
# vectorstore.save_local("acapglobal_index")
# print('vectorstore: save_local')

#
# End When refresh document


chat_history = []
#index = FAISS.load_local(index_path, embeddings)
index = FAISS.load_local(index_path, embeddings)

qa = ConversationalRetrievalChain.from_llm(llm, index.as_retriever(), max_tokens_limit=400)

def search_query_data(s_query):
    print("search_query:")
    print( s_query)
    retrieval=qa({"question": s_query, "chat_history": chat_history})
    return retrieval